Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations2500
Missing cells143
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory332.2 KiB
Average record size in memory136.1 B

Variable types

Categorical3
Numeric14

Alerts

Assembly_Line_No is highly overall correlated with Machine_IDHigh correlation
Coolant_Pressure(bar) is highly overall correlated with DowntimeHigh correlation
Cutting(kN) is highly overall correlated with DowntimeHigh correlation
Downtime is highly overall correlated with Coolant_Pressure(bar) and 4 other fieldsHigh correlation
Hydraulic_Pressure(bar) is highly overall correlated with DowntimeHigh correlation
Machine_ID is highly overall correlated with Assembly_Line_NoHigh correlation
Spindle_Speed(RPM) is highly overall correlated with DowntimeHigh correlation
Torque(Nm) is highly overall correlated with DowntimeHigh correlation

Reproduction

Analysis started2024-12-05 06:41:26.998431
Analysis finished2024-12-05 06:41:42.096164
Duration15.1 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Machine_ID
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
Makino-L1-Unit1-2013
874 
Makino-L3-Unit1-2015
818 
Makino-L2-Unit1-2015
808 

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters50000
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMakino-L1-Unit1-2013
2nd rowMakino-L1-Unit1-2013
3rd rowMakino-L3-Unit1-2015
4th rowMakino-L2-Unit1-2015
5th rowMakino-L1-Unit1-2013

Common Values

ValueCountFrequency (%)
Makino-L1-Unit1-2013 874
35.0%
Makino-L3-Unit1-2015 818
32.7%
Makino-L2-Unit1-2015 808
32.3%

Length

2024-12-05T01:41:42.173892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-05T01:41:42.246413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
makino-l1-unit1-2013 874
35.0%
makino-l3-unit1-2015 818
32.7%
makino-l2-unit1-2015 808
32.3%

Most occurring characters

ValueCountFrequency (%)
- 7500
15.0%
1 5874
11.7%
i 5000
10.0%
n 5000
10.0%
2 3308
 
6.6%
M 2500
 
5.0%
a 2500
 
5.0%
k 2500
 
5.0%
o 2500
 
5.0%
L 2500
 
5.0%
Other values (5) 10818
21.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20000
40.0%
Decimal Number 15000
30.0%
Dash Punctuation 7500
 
15.0%
Uppercase Letter 7500
 
15.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 5000
25.0%
n 5000
25.0%
a 2500
12.5%
k 2500
12.5%
o 2500
12.5%
t 2500
12.5%
Decimal Number
ValueCountFrequency (%)
1 5874
39.2%
2 3308
22.1%
0 2500
16.7%
3 1692
 
11.3%
5 1626
 
10.8%
Uppercase Letter
ValueCountFrequency (%)
M 2500
33.3%
L 2500
33.3%
U 2500
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 7500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27500
55.0%
Common 22500
45.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 5000
18.2%
n 5000
18.2%
M 2500
9.1%
a 2500
9.1%
k 2500
9.1%
o 2500
9.1%
L 2500
9.1%
U 2500
9.1%
t 2500
9.1%
Common
ValueCountFrequency (%)
- 7500
33.3%
1 5874
26.1%
2 3308
14.7%
0 2500
 
11.1%
3 1692
 
7.5%
5 1626
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 7500
15.0%
1 5874
11.7%
i 5000
10.0%
n 5000
10.0%
2 3308
 
6.6%
M 2500
 
5.0%
a 2500
 
5.0%
k 2500
 
5.0%
o 2500
 
5.0%
L 2500
 
5.0%
Other values (5) 10818
21.6%

Assembly_Line_No
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
Shopfloor-L1
874 
Shopfloor-L3
818 
Shopfloor-L2
808 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters30000
Distinct characters12
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowShopfloor-L1
2nd rowShopfloor-L1
3rd rowShopfloor-L3
4th rowShopfloor-L2
5th rowShopfloor-L1

Common Values

ValueCountFrequency (%)
Shopfloor-L1 874
35.0%
Shopfloor-L3 818
32.7%
Shopfloor-L2 808
32.3%

Length

2024-12-05T01:41:42.331067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-05T01:41:42.399137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
shopfloor-l1 874
35.0%
shopfloor-l3 818
32.7%
shopfloor-l2 808
32.3%

Most occurring characters

ValueCountFrequency (%)
o 7500
25.0%
S 2500
 
8.3%
h 2500
 
8.3%
p 2500
 
8.3%
f 2500
 
8.3%
l 2500
 
8.3%
r 2500
 
8.3%
- 2500
 
8.3%
L 2500
 
8.3%
1 874
 
2.9%
Other values (2) 1626
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20000
66.7%
Uppercase Letter 5000
 
16.7%
Dash Punctuation 2500
 
8.3%
Decimal Number 2500
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 7500
37.5%
h 2500
 
12.5%
p 2500
 
12.5%
f 2500
 
12.5%
l 2500
 
12.5%
r 2500
 
12.5%
Decimal Number
ValueCountFrequency (%)
1 874
35.0%
3 818
32.7%
2 808
32.3%
Uppercase Letter
ValueCountFrequency (%)
S 2500
50.0%
L 2500
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 2500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 25000
83.3%
Common 5000
 
16.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 7500
30.0%
S 2500
 
10.0%
h 2500
 
10.0%
p 2500
 
10.0%
f 2500
 
10.0%
l 2500
 
10.0%
r 2500
 
10.0%
L 2500
 
10.0%
Common
ValueCountFrequency (%)
- 2500
50.0%
1 874
 
17.5%
3 818
 
16.4%
2 808
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 7500
25.0%
S 2500
 
8.3%
h 2500
 
8.3%
p 2500
 
8.3%
f 2500
 
8.3%
l 2500
 
8.3%
r 2500
 
8.3%
- 2500
 
8.3%
L 2500
 
8.3%
1 874
 
2.9%
Other values (2) 1626
 
5.4%

Hydraulic_Pressure(bar)
Real number (ℝ)

High correlation 

Distinct1977
Distinct (%)79.4%
Missing10
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean101.40908
Minimum-14.326454
Maximum191
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size19.7 KiB
2024-12-05T01:41:42.491267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-14.326454
5-th percentile56.959
Q176.355
median96.763486
Q3126.41573
95-th percentile149.3864
Maximum191
Range205.32645
Interquartile range (IQR)50.060727

Descriptive statistics

Standard deviation30.289301
Coefficient of variation (CV)0.29868429
Kurtosis-0.92029181
Mean101.40908
Median Absolute Deviation (MAD)25.096514
Skewness0.19707551
Sum252508.62
Variance917.44173
MonotonicityNot monotonic
2024-12-05T01:41:42.593803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.4587468 20
 
0.8%
88.27913423 20
 
0.8%
89.4587468 20
 
0.8%
82.9688079 19
 
0.8%
81.1382212 19
 
0.8%
80.726488 19
 
0.8%
73.5721609 18
 
0.7%
92.8557399 18
 
0.7%
80.27 16
 
0.6%
71.04 16
 
0.6%
Other values (1967) 2305
92.2%
ValueCountFrequency (%)
-14.32645418 1
< 0.1%
15.32576081 1
< 0.1%
50.14 1
< 0.1%
50.15 2
0.1%
50.16 1
< 0.1%
50.23 2
0.1%
50.24 1
< 0.1%
50.36 1
< 0.1%
50.39 1
< 0.1%
50.41 1
< 0.1%
ValueCountFrequency (%)
191 1
< 0.1%
179.8860999 1
< 0.1%
176.2184501 1
< 0.1%
175.7562785 1
< 0.1%
175.1 1
< 0.1%
174.7032604 1
< 0.1%
174.2498658 1
< 0.1%
173.3260248 1
< 0.1%
173.22 1
< 0.1%
173.0714604 1
< 0.1%

Coolant_Pressure(bar)
Real number (ℝ)

High correlation 

Distinct1628
Distinct (%)65.6%
Missing19
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean4.9470585
Minimum0.325
Maximum11.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-12-05T01:41:42.702335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.325
5-th percentile3.10696
Q14.4624635
median4.9399602
Q35.5184012
95-th percentile6.8639441
Maximum11.35
Range11.025
Interquartile range (IQR)1.0559377

Descriptive statistics

Standard deviation0.99735718
Coefficient of variation (CV)0.2016061
Kurtosis1.169455
Mean4.9470585
Median Absolute Deviation (MAD)0.52872994
Skewness0.14708131
Sum12273.652
Variance0.99472135
MonotonicityNot monotonic
2024-12-05T01:41:42.805868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.84252053 50
 
2.0%
6.863944117 50
 
2.0%
6.839413159 50
 
2.0%
6.893181921 50
 
2.0%
5.918357337 50
 
2.0%
6.560332199 50
 
2.0%
5.567856573 50
 
2.0%
4.566853902 50
 
2.0%
3.126011472 40
 
1.6%
3.106960002 40
 
1.6%
Other values (1618) 2001
80.0%
ValueCountFrequency (%)
0.325 1
 
< 0.1%
3.053148837 39
1.6%
3.053451378 35
1.4%
3.105982448 33
1.3%
3.106960002 40
1.6%
3.126011472 40
1.6%
3.140995239 8
 
0.3%
3.164804649 40
1.6%
3.166083167 8
 
0.3%
3.166768986 8
 
0.3%
ValueCountFrequency (%)
11.35 1
 
< 0.1%
11.3 1
 
< 0.1%
6.960635908 26
1.0%
6.959532755 1
 
< 0.1%
6.933724915 1
 
< 0.1%
6.893181921 50
2.0%
6.878521595 1
 
< 0.1%
6.863944117 50
2.0%
6.839413159 50
2.0%
6.823141776 1
 
< 0.1%

Air_System_Pressure(bar)
Real number (ℝ)

Distinct2472
Distinct (%)99.6%
Missing17
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean6.4992746
Minimum5.06348
Maximum7.9739915
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-12-05T01:41:42.925400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5.06348
5-th percentile5.816886
Q16.2179865
median6.5051914
Q36.7805504
95-th percentile7.1473785
Maximum7.9739915
Range2.9105115
Interquartile range (IQR)0.56256382

Descriptive statistics

Standard deviation0.40727941
Coefficient of variation (CV)0.062665365
Kurtosis-0.0015779915
Mean6.4992746
Median Absolute Deviation (MAD)0.28182502
Skewness-0.052899493
Sum16137.699
Variance0.16587652
MonotonicityNot monotonic
2024-12-05T01:41:43.034933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.430073321 2
 
0.1%
6.668244826 2
 
0.1%
5.628717474 2
 
0.1%
6.940606305 2
 
0.1%
6.836188036 2
 
0.1%
5.928643257 2
 
0.1%
6.753559682 2
 
0.1%
6.011482601 2
 
0.1%
6.167548538 2
 
0.1%
5.955234644 2
 
0.1%
Other values (2462) 2463
98.5%
(Missing) 17
 
0.7%
ValueCountFrequency (%)
5.063480035 1
< 0.1%
5.091411159 1
< 0.1%
5.151876018 1
< 0.1%
5.283833449 1
< 0.1%
5.305505788 1
< 0.1%
5.329776651 1
< 0.1%
5.366874865 1
< 0.1%
5.384449305 1
< 0.1%
5.399351647 1
< 0.1%
5.4183556 1
< 0.1%
ValueCountFrequency (%)
7.973991528 1
< 0.1%
7.971606819 1
< 0.1%
7.804750131 1
< 0.1%
7.781150882 1
< 0.1%
7.712440729 1
< 0.1%
7.644785584 1
< 0.1%
7.620960149 1
< 0.1%
7.616765583 1
< 0.1%
7.588085533 1
< 0.1%
7.584961608 1
< 0.1%

Coolant_Temperature
Real number (ℝ)

Distinct275
Distinct (%)11.1%
Missing12
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean18.559887
Minimum4.1
Maximum98.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-12-05T01:41:43.166475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4.1
5-th percentile5.7
Q110.4
median21.2
Q325.6
95-th percentile29.865
Maximum98.2
Range94.1
Interquartile range (IQR)15.2

Descriptive statistics

Standard deviation8.5544797
Coefficient of variation (CV)0.46091226
Kurtosis1.5646387
Mean18.559887
Median Absolute Deviation (MAD)6.65
Skewness0.10830136
Sum46177
Variance73.179122
MonotonicityNot monotonic
2024-12-05T01:41:43.277529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.4 83
 
3.3%
25.6 82
 
3.3%
27 72
 
2.9%
6.8 71
 
2.8%
24.4 70
 
2.8%
29.8 70
 
2.8%
30.3 69
 
2.8%
28.7 68
 
2.7%
4.5 68
 
2.7%
11.9 67
 
2.7%
Other values (265) 1768
70.7%
ValueCountFrequency (%)
4.1 6
 
0.2%
4.2 5
 
0.2%
4.3 2
 
0.1%
4.4 3
 
0.1%
4.5 68
2.7%
4.6 2
 
0.1%
4.7 1
 
< 0.1%
4.8 2
 
0.1%
4.9 7
 
0.3%
5 6
 
0.2%
ValueCountFrequency (%)
98.2 1
 
< 0.1%
36.5 3
0.1%
36.1 1
 
< 0.1%
36 1
 
< 0.1%
35.6 1
 
< 0.1%
35.5 2
0.1%
35.3 1
 
< 0.1%
34.9 1
 
< 0.1%
34.7 1
 
< 0.1%
34.3 1
 
< 0.1%

Hydraulic_Oil_Temperature
Real number (ℝ)

Distinct209
Distinct (%)8.4%
Missing16
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean47.618317
Minimum35.2
Maximum61.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-12-05T01:41:43.388063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum35.2
5-th percentile41.4
Q145.1
median47.7
Q350.1
95-th percentile53.7
Maximum61.4
Range26.2
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.7686744
Coefficient of variation (CV)0.079143375
Kurtosis0.038164047
Mean47.618317
Median Absolute Deviation (MAD)2.5
Skewness-0.0022905892
Sum118283.9
Variance14.202906
MonotonicityNot monotonic
2024-12-05T01:41:43.508595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.5 32
 
1.3%
48 32
 
1.3%
50 32
 
1.3%
49.3 31
 
1.2%
44.8 30
 
1.2%
47.3 30
 
1.2%
48.5 30
 
1.2%
46.2 30
 
1.2%
45.4 30
 
1.2%
47.9 29
 
1.2%
Other values (199) 2178
87.1%
ValueCountFrequency (%)
35.2 1
< 0.1%
35.8 1
< 0.1%
36.1 1
< 0.1%
36.2 1
< 0.1%
36.4 1
< 0.1%
36.9 1
< 0.1%
37.1 1
< 0.1%
37.4 1
< 0.1%
37.5 1
< 0.1%
37.6 1
< 0.1%
ValueCountFrequency (%)
61.4 1
< 0.1%
60.3 1
< 0.1%
59.5 2
0.1%
59.2 2
0.1%
59.1 1
< 0.1%
58.7 1
< 0.1%
58.1 1
< 0.1%
57.9 2
0.1%
57.7 1
< 0.1%
57.4 1
< 0.1%
Distinct204
Distinct (%)8.2%
Missing7
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean35.063698
Minimum22.6
Maximum49.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-12-05T01:41:43.626128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum22.6
5-th percentile28.9
Q132.5
median35.1
Q337.6
95-th percentile41.1
Maximum49.5
Range26.9
Interquartile range (IQR)5.1

Descriptive statistics

Standard deviation3.7648233
Coefficient of variation (CV)0.10737097
Kurtosis-0.043862344
Mean35.063698
Median Absolute Deviation (MAD)2.5
Skewness-0.035942013
Sum87413.8
Variance14.173894
MonotonicityNot monotonic
2024-12-05T01:41:43.729659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.6 36
 
1.4%
35.2 35
 
1.4%
36.2 34
 
1.4%
34.1 32
 
1.3%
33.7 32
 
1.3%
34.9 32
 
1.3%
37.5 32
 
1.3%
34 31
 
1.2%
33.8 31
 
1.2%
35.3 31
 
1.2%
Other values (194) 2167
86.7%
ValueCountFrequency (%)
22.6 1
< 0.1%
23.2 1
< 0.1%
23.4 1
< 0.1%
23.5 1
< 0.1%
23.8 1
< 0.1%
24 2
0.1%
24.1 1
< 0.1%
24.8 1
< 0.1%
24.9 2
0.1%
25.2 2
0.1%
ValueCountFrequency (%)
49.5 1
< 0.1%
46.3 1
< 0.1%
46 1
< 0.1%
45.8 1
< 0.1%
45.7 1
< 0.1%
45.6 1
< 0.1%
45.4 1
< 0.1%
45.1 2
0.1%
44.9 2
0.1%
44.5 1
< 0.1%

Spindle_Vibration
Real number (ℝ)

Distinct1144
Distinct (%)46.0%
Missing11
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean1.0093343
Minimum-0.461
Maximum2
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size19.7 KiB
2024-12-05T01:41:43.845191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.461
5-th percentile0.4392
Q10.777
median1.008
Q31.236
95-th percentile1.593
Maximum2
Range2.461
Interquartile range (IQR)0.459

Descriptive statistics

Standard deviation0.34289805
Coefficient of variation (CV)0.33972695
Kurtosis-0.018718168
Mean1.0093343
Median Absolute Deviation (MAD)0.229
Skewness0.0015321174
Sum2512.233
Variance0.11757908
MonotonicityNot monotonic
2024-12-05T01:41:43.948719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.231 9
 
0.4%
1.45 8
 
0.3%
0.892 7
 
0.3%
1.15 7
 
0.3%
0.986 7
 
0.3%
0.946 7
 
0.3%
0.879 7
 
0.3%
0.855 7
 
0.3%
0.903 7
 
0.3%
0.951 7
 
0.3%
Other values (1134) 2416
96.6%
(Missing) 11
 
0.4%
ValueCountFrequency (%)
-0.461 1
 
< 0.1%
0.01 3
0.1%
0.012 1
 
< 0.1%
0.033 1
 
< 0.1%
0.076 1
 
< 0.1%
0.088 1
 
< 0.1%
0.107 1
 
< 0.1%
0.141 1
 
< 0.1%
0.156 1
 
< 0.1%
0.159 1
 
< 0.1%
ValueCountFrequency (%)
2 2
0.1%
1.994 1
< 0.1%
1.984 1
< 0.1%
1.971 1
< 0.1%
1.942 1
< 0.1%
1.929 1
< 0.1%
1.923 1
< 0.1%
1.917 1
< 0.1%
1.899 1
< 0.1%
1.892 1
< 0.1%

Tool_Vibration
Real number (ℝ)

Distinct2350
Distinct (%)94.4%
Missing11
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean25.411975
Minimum2.161
Maximum45.726
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-12-05T01:41:44.062258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.161
5-th percentile14.9522
Q121.089
median25.455
Q329.79
95-th percentile35.807
Maximum45.726
Range43.565
Interquartile range (IQR)8.701

Descriptive statistics

Standard deviation6.4371418
Coefficient of variation (CV)0.25331135
Kurtosis0.0064506654
Mean25.411975
Median Absolute Deviation (MAD)4.353
Skewness-0.061005233
Sum63250.406
Variance41.436795
MonotonicityNot monotonic
2024-12-05T01:41:44.170793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.585 3
 
0.1%
26.736 3
 
0.1%
24.991 2
 
0.1%
20.646 2
 
0.1%
25.98 2
 
0.1%
19.765 2
 
0.1%
27.324 2
 
0.1%
24.015 2
 
0.1%
24.874 2
 
0.1%
29.235 2
 
0.1%
Other values (2340) 2467
98.7%
(Missing) 11
 
0.4%
ValueCountFrequency (%)
2.161 1
< 0.1%
3.469 1
< 0.1%
5.775 1
< 0.1%
5.892 1
< 0.1%
6.338 1
< 0.1%
6.539 1
< 0.1%
6.886 1
< 0.1%
6.977 1
< 0.1%
7.286 1
< 0.1%
7.817 1
< 0.1%
ValueCountFrequency (%)
45.726 1
< 0.1%
45.492 1
< 0.1%
44.839 1
< 0.1%
44.204 1
< 0.1%
43.893 1
< 0.1%
43.624 1
< 0.1%
42.901 1
< 0.1%
42.693 1
< 0.1%
42.45 1
< 0.1%
42.262 1
< 0.1%

Spindle_Speed(RPM)
Real number (ℝ)

High correlation 

Distinct1180
Distinct (%)47.3%
Missing6
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean20274.792
Minimum0
Maximum27957
Zeros7
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-12-05T01:41:44.286478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14152
Q117919
median20137.5
Q322501.75
95-th percentile27613
Maximum27957
Range27957
Interquartile range (IQR)4582.75

Descriptive statistics

Standard deviation3852.6601
Coefficient of variation (CV)0.19002217
Kurtosis1.3447687
Mean20274.792
Median Absolute Deviation (MAD)2229
Skewness-0.17247517
Sum50565332
Variance14842990
MonotonicityNot monotonic
2024-12-05T01:41:44.389892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17726 93
 
3.7%
26526 76
 
3.0%
27613 76
 
3.0%
26605 76
 
3.0%
23432 76
 
3.0%
20504 76
 
3.0%
21951 76
 
3.0%
18436 68
 
2.7%
16198 68
 
2.7%
13272 65
 
2.6%
Other values (1170) 1744
69.8%
ValueCountFrequency (%)
0 7
 
0.3%
12788 1
 
< 0.1%
13168 1
 
< 0.1%
13272 65
2.6%
13617 1
 
< 0.1%
13676 1
 
< 0.1%
13704 1
 
< 0.1%
13766 1
 
< 0.1%
13853 1
 
< 0.1%
13888 1
 
< 0.1%
ValueCountFrequency (%)
27957 52
2.1%
27882 1
 
< 0.1%
27613 76
3.0%
27598 1
 
< 0.1%
27559 1
 
< 0.1%
27333 1
 
< 0.1%
27111 1
 
< 0.1%
27055 1
 
< 0.1%
27025 1
 
< 0.1%
27005 1
 
< 0.1%

Voltage(volts)
Real number (ℝ)

Distinct238
Distinct (%)9.5%
Missing6
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean348.99679
Minimum202
Maximum479
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-12-05T01:41:44.503428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum202
5-th percentile274.65
Q1319
median349
Q3380
95-th percentile422
Maximum479
Range277
Interquartile range (IQR)61

Descriptive statistics

Standard deviation45.376024
Coefficient of variation (CV)0.13001846
Kurtosis-0.09032812
Mean348.99679
Median Absolute Deviation (MAD)30
Skewness-0.028657886
Sum870398
Variance2058.9835
MonotonicityNot monotonic
2024-12-05T01:41:44.603956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
337 29
 
1.2%
360 28
 
1.1%
341 28
 
1.1%
334 28
 
1.1%
362 28
 
1.1%
363 27
 
1.1%
354 26
 
1.0%
349 26
 
1.0%
356 25
 
1.0%
361 25
 
1.0%
Other values (228) 2224
89.0%
ValueCountFrequency (%)
202 2
0.1%
207 1
 
< 0.1%
211 1
 
< 0.1%
221 1
 
< 0.1%
223 1
 
< 0.1%
225 1
 
< 0.1%
227 1
 
< 0.1%
228 1
 
< 0.1%
229 1
 
< 0.1%
230 3
0.1%
ValueCountFrequency (%)
479 2
0.1%
477 1
 
< 0.1%
475 1
 
< 0.1%
473 2
0.1%
472 1
 
< 0.1%
471 2
0.1%
470 1
 
< 0.1%
466 3
0.1%
464 2
0.1%
462 2
0.1%

Torque(Nm)
Real number (ℝ)

High correlation 

Distinct1327
Distinct (%)53.5%
Missing21
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean25.234968
Minimum0
Maximum55.5524
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-12-05T01:41:44.701487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.119739
Q121.666115
median24.647736
Q330.514008
95-th percentile35.580334
Maximum55.5524
Range55.5524
Interquartile range (IQR)8.8478935

Descriptive statistics

Standard deviation6.1385635
Coefficient of variation (CV)0.24325625
Kurtosis-0.46562796
Mean25.234968
Median Absolute Deviation (MAD)3.9033075
Skewness0.030577923
Sum62557.485
Variance37.681962
MonotonicityNot monotonic
2024-12-05T01:41:44.802012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.58033366 79
 
3.2%
34.97300419 78
 
3.1%
33.92336502 77
 
3.1%
32.37045571 73
 
2.9%
35.5812209 73
 
2.9%
15.90071615 64
 
2.6%
17.89931102 64
 
2.6%
16.96410507 64
 
2.6%
16.44955444 64
 
2.6%
15.1197393 64
 
2.6%
Other values (1317) 1779
71.2%
ValueCountFrequency (%)
0 2
 
0.1%
14.10999444 17
 
0.7%
14.20288973 64
2.6%
14.44705039 17
 
0.7%
15.1197393 64
2.6%
15.90071615 64
2.6%
16.44955444 64
2.6%
16.96410507 64
2.6%
17.1222294 1
 
< 0.1%
17.89931102 64
2.6%
ValueCountFrequency (%)
55.5524 1
 
< 0.1%
36.25470838 1
 
< 0.1%
35.74438455 1
 
< 0.1%
35.5812209 73
2.9%
35.58033366 79
3.2%
35.34478419 1
 
< 0.1%
35.05926416 1
 
< 0.1%
34.97300419 78
3.1%
34.83669002 1
 
< 0.1%
33.92336502 77
3.1%

Cutting(kN)
Real number (ℝ)

High correlation 

Distinct170
Distinct (%)6.8%
Missing7
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean2.7825511
Minimum1.8
Maximum3.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2024-12-05T01:41:44.921539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile1.87
Q12.25
median2.78
Q33.27
95-th percentile3.69
Maximum3.93
Range2.13
Interquartile range (IQR)1.02

Descriptive statistics

Standard deviation0.61668826
Coefficient of variation (CV)0.22162693
Kurtosis-1.0871706
Mean2.7825511
Median Absolute Deviation (MAD)0.51
Skewness0.11396278
Sum6936.9
Variance0.3803044
MonotonicityNot monotonic
2024-12-05T01:41:45.015063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.55 104
 
4.2%
3.67 104
 
4.2%
2.88 62
 
2.5%
2.02 59
 
2.4%
3 59
 
2.4%
1.99 57
 
2.3%
1.88 54
 
2.2%
1.95 54
 
2.2%
3.61 50
 
2.0%
3.58 50
 
2.0%
Other values (160) 1840
73.6%
ValueCountFrequency (%)
1.8 1
 
< 0.1%
1.85 45
1.8%
1.86 45
1.8%
1.87 45
1.8%
1.88 54
2.2%
1.89 27
1.1%
1.91 45
1.8%
1.92 1
 
< 0.1%
1.94 1
 
< 0.1%
1.95 54
2.2%
ValueCountFrequency (%)
3.93 49
2.0%
3.91 49
2.0%
3.78 1
 
< 0.1%
3.72 1
 
< 0.1%
3.69 50
2.0%
3.67 104
4.2%
3.65 4
 
0.2%
3.63 1
 
< 0.1%
3.62 1
 
< 0.1%
3.61 50
2.0%

Downtime
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
Machine_Failure
1265 
No_Machine_Failure
1235 

Length

Max length18
Median length15
Mean length16.482
Min length15

Characters and Unicode

Total characters41205
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMachine_Failure
2nd rowMachine_Failure
3rd rowMachine_Failure
4th rowMachine_Failure
5th rowMachine_Failure

Common Values

ValueCountFrequency (%)
Machine_Failure 1265
50.6%
No_Machine_Failure 1235
49.4%

Length

2024-12-05T01:41:45.108594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-05T01:41:45.195118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
machine_failure 1265
50.6%
no_machine_failure 1235
49.4%

Most occurring characters

ValueCountFrequency (%)
a 5000
12.1%
i 5000
12.1%
e 5000
12.1%
_ 3735
9.1%
M 2500
 
6.1%
c 2500
 
6.1%
h 2500
 
6.1%
n 2500
 
6.1%
F 2500
 
6.1%
l 2500
 
6.1%
Other values (4) 7470
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31235
75.8%
Uppercase Letter 6235
 
15.1%
Connector Punctuation 3735
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5000
16.0%
i 5000
16.0%
e 5000
16.0%
c 2500
8.0%
h 2500
8.0%
n 2500
8.0%
l 2500
8.0%
u 2500
8.0%
r 2500
8.0%
o 1235
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
M 2500
40.1%
F 2500
40.1%
N 1235
19.8%
Connector Punctuation
ValueCountFrequency (%)
_ 3735
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37470
90.9%
Common 3735
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5000
13.3%
i 5000
13.3%
e 5000
13.3%
M 2500
6.7%
c 2500
6.7%
h 2500
6.7%
n 2500
6.7%
F 2500
6.7%
l 2500
6.7%
u 2500
6.7%
Other values (3) 4970
13.3%
Common
ValueCountFrequency (%)
_ 3735
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41205
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 5000
12.1%
i 5000
12.1%
e 5000
12.1%
_ 3735
9.1%
M 2500
 
6.1%
c 2500
 
6.1%
h 2500
 
6.1%
n 2500
 
6.1%
F 2500
 
6.1%
l 2500
 
6.1%
Other values (4) 7470
18.1%

month_sin
Real number (ℝ)

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86696502
Minimum-0.5
Maximum1
Zeros0
Zeros (%)0.0%
Negative26
Negative (%)1.0%
Memory size19.7 KiB
2024-12-05T01:41:45.260118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.5
5-th percentile0.5
Q10.8660254
median0.8660254
Q31
95-th percentile1
Maximum1
Range1.5
Interquartile range (IQR)0.1339746

Descriptive statistics

Standard deviation0.18543302
Coefficient of variation (CV)0.21388755
Kurtosis7.4187791
Mean0.86696502
Median Absolute Deviation (MAD)0.1339746
Skewness-2.3881108
Sum2167.4126
Variance0.034385406
MonotonicityNot monotonic
2024-12-05T01:41:45.339645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 1048
41.9%
0.8660254038 597
23.9%
0.8660254038 527
21.1%
0.5 294
 
11.8%
-2.449293598 × 10-1624
 
1.0%
1.224646799 × 10-168
 
0.3%
-0.5 1
 
< 0.1%
-0.5 1
 
< 0.1%
ValueCountFrequency (%)
-0.5 1
 
< 0.1%
-0.5 1
 
< 0.1%
-2.449293598 × 10-1624
 
1.0%
1.224646799 × 10-168
 
0.3%
0.5 294
 
11.8%
0.8660254038 597
23.9%
0.8660254038 527
21.1%
1 1048
41.9%
ValueCountFrequency (%)
1 1048
41.9%
0.8660254038 527
21.1%
0.8660254038 597
23.9%
0.5 294
 
11.8%
1.224646799 × 10-168
 
0.3%
-2.449293598 × 10-1624
 
1.0%
-0.5 1
 
< 0.1%
-0.5 1
 
< 0.1%

month_cos
Real number (ℝ)

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04187743
Minimum-1
Maximum1
Zeros0
Zeros (%)0.0%
Negative652
Negative (%)26.1%
Memory size19.7 KiB
2024-12-05T01:41:45.412174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-0.51830127
Q1-0.5
median6.123234 × 10-17
Q30.5
95-th percentile0.8660254
Maximum1
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.46079411
Coefficient of variation (CV)11.0034
Kurtosis-0.65952991
Mean0.04187743
Median Absolute Deviation (MAD)0.5
Skewness-0.0074736818
Sum104.69358
Variance0.21233121
MonotonicityNot monotonic
2024-12-05T01:41:45.486133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
6.123233996 × 10-171048
41.9%
0.5 597
23.9%
-0.5 527
21.1%
0.8660254038 178
 
7.1%
-0.8660254038 116
 
4.6%
1 24
 
1.0%
-1 8
 
0.3%
0.8660254038 1
 
< 0.1%
-0.8660254038 1
 
< 0.1%
ValueCountFrequency (%)
-1 8
 
0.3%
-0.8660254038 1
 
< 0.1%
-0.8660254038 116
 
4.6%
-0.5 527
21.1%
6.123233996 × 10-171048
41.9%
0.5 597
23.9%
0.8660254038 1
 
< 0.1%
0.8660254038 178
 
7.1%
1 24
 
1.0%
ValueCountFrequency (%)
1 24
 
1.0%
0.8660254038 178
 
7.1%
0.8660254038 1
 
< 0.1%
0.5 597
23.9%
6.123233996 × 10-171048
41.9%
-0.5 527
21.1%
-0.8660254038 116
 
4.6%
-0.8660254038 1
 
< 0.1%
-1 8
 
0.3%

Interactions

2024-12-05T01:41:40.419608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:27.419616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:28.293831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:29.155875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:30.314578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:31.216163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:32.130902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:33.068661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:34.215977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:35.164730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:36.186519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:37.212325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:38.190405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:39.063853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:40.494624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:27.489136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:28.351246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:29.226896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:30.373103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:31.278685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:32.198428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:33.129180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:34.271986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:35.236247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:36.256519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:37.274555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:38.254013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:39.135374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:40.586532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:27.550134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:28.411967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:29.517509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:30.443832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:31.339685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:32.259441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:33.190705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:34.329505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:35.313780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:36.331052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:37.344029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:38.321155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:39.216896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:40.672538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:27.613661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:28.473758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:29.582447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:30.523403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:31.414213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:32.330958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:33.252705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:34.400033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:35.388303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:36.409583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:37.416596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:38.383558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:39.597576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:40.761068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:27.673182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:28.536155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:29.647374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:30.582923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:31.481743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:32.400484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:33.313232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:34.460044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:35.459303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:36.480114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:37.502231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:38.445183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:39.662319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:40.852586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:27.732183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:28.597732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:29.712790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:30.650923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:31.542746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:32.465492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:33.379762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:34.540562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:35.537831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:36.551111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:37.591970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:38.506853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:39.730171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:40.937118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:27.795706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:28.661004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:29.780963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:30.715447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:31.606268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:32.526019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:33.444763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:34.598086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:35.603355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:36.621637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:37.656652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:38.569206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:39.794482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:41.019643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:27.861631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:28.725490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:29.845963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:30.778482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:31.684799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:32.592539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:33.520291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:34.663096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:35.675886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:36.696164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:37.730232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:38.631480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:39.858991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:41.093900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:27.920385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:28.782845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:29.919484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:30.836480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:31.741796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:32.650538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:33.577818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:34.720618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:35.749886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:36.758164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:37.790007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:38.689571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:39.927880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:41.173908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:27.986798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:28.845295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:29.990013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:30.897002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:31.811322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:32.718073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:33.894406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:34.795141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:35.823410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:36.840689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:37.855077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:38.746566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:40.008402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:41.246427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:28.048408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:28.906356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:30.054014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:30.963558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:31.873840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:32.780595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:33.955401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:34.874151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:35.896938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:36.911217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:37.916167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:38.811044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:40.083928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:41.318951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:28.108011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:28.966764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:30.117536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:31.025123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:31.934856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:32.845596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:34.023925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:34.933670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:35.970947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:36.972224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:37.975959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:38.871664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:40.161932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:41.390481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:28.163292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:29.025335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:30.178054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:31.084645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:31.993378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:32.922125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:34.080453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:35.004194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:36.037465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:37.045741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:38.041386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:38.932358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:40.242451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:41.467489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:28.227394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:29.087753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:30.245056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:31.149643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:32.061394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:32.998650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:34.145453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:35.083722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:36.110994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:37.127274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:38.110856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:38.995433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-05T01:41:40.334973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-12-05T01:41:45.555037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Air_System_Pressure(bar)Assembly_Line_NoCoolant_Pressure(bar)Coolant_TemperatureCutting(kN)DowntimeHydraulic_Oil_TemperatureHydraulic_Pressure(bar)Machine_IDSpindle_Bearing_TemperatureSpindle_Speed(RPM)Spindle_VibrationTool_VibrationTorque(Nm)Voltage(volts)month_cosmonth_sin
Air_System_Pressure(bar)1.0000.000-0.0280.0430.0000.000-0.024-0.0260.0000.016-0.0000.0110.0560.0360.006-0.0180.008
Assembly_Line_No0.0001.0000.0000.0000.0220.0000.0330.0431.0000.0000.0000.0180.0120.0000.0100.0110.000
Coolant_Pressure(bar)-0.0280.0001.000-0.0160.1110.5660.0090.0280.000-0.0130.055-0.000-0.001-0.0360.0290.118-0.061
Coolant_Temperature0.0430.000-0.0161.0000.1140.1970.011-0.1400.000-0.0140.0950.0070.001-0.0710.0080.0240.034
Cutting(kN)0.0000.0220.1110.1141.0000.826-0.015-0.1910.0220.0010.199-0.017-0.042-0.116-0.0390.090-0.064
Downtime0.0000.0000.5660.1970.8261.0000.0000.6180.0000.0230.5060.0550.0000.5710.0000.0000.021
Hydraulic_Oil_Temperature-0.0240.0330.0090.011-0.0150.0001.0000.0100.0330.0300.005-0.002-0.009-0.0060.032-0.0280.029
Hydraulic_Pressure(bar)-0.0260.0430.028-0.140-0.1910.6180.0101.0000.0430.021-0.080-0.0060.0180.096-0.001-0.0120.004
Machine_ID0.0001.0000.0000.0000.0220.0000.0330.0431.0000.0000.0000.0180.0120.0000.0100.0110.000
Spindle_Bearing_Temperature0.0160.000-0.013-0.0140.0010.0230.0300.0210.0001.0000.029-0.035-0.0240.0000.009-0.006-0.034
Spindle_Speed(RPM)-0.0000.0000.0550.0950.1990.5060.005-0.0800.0000.0291.0000.015-0.000-0.156-0.0200.004-0.020
Spindle_Vibration0.0110.018-0.0000.007-0.0170.055-0.002-0.0060.018-0.0350.0151.000-0.0120.0210.0090.0060.015
Tool_Vibration0.0560.012-0.0010.001-0.0420.000-0.0090.0180.012-0.024-0.000-0.0121.000-0.0190.023-0.004-0.011
Torque(Nm)0.0360.000-0.036-0.071-0.1160.571-0.0060.0960.0000.000-0.1560.021-0.0191.0000.014-0.0540.083
Voltage(volts)0.0060.0100.0290.008-0.0390.0000.032-0.0010.0100.009-0.0200.0090.0230.0141.0000.007-0.001
month_cos-0.0180.0110.1180.0240.0900.000-0.028-0.0120.011-0.0060.0040.006-0.004-0.0540.0071.000-0.341
month_sin0.0080.000-0.0610.034-0.0640.0210.0290.0040.000-0.034-0.0200.015-0.0110.083-0.001-0.3411.000

Missing values

2024-12-05T01:41:41.590533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-05T01:41:41.788585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-05T01:41:41.964118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Machine_IDAssembly_Line_NoHydraulic_Pressure(bar)Coolant_Pressure(bar)Air_System_Pressure(bar)Coolant_TemperatureHydraulic_Oil_TemperatureSpindle_Bearing_TemperatureSpindle_VibrationTool_VibrationSpindle_Speed(RPM)Voltage(volts)Torque(Nm)Cutting(kN)Downtimemonth_sinmonth_cos
0Makino-L1-Unit1-2013Shopfloor-L171.0400006.9337256.28496525.646.033.41.29126.49225892.0335.024.0553263.58Machine_Failure-2.449294e-161.000000e+00
1Makino-L1-Unit1-2013Shopfloor-L1125.3300004.9368926.19673335.347.434.61.38225.27419856.0368.014.2028902.68Machine_Failure-2.449294e-161.000000e+00
2Makino-L3-Unit1-2015Shopfloor-L371.1200006.8394136.65544813.140.733.01.31930.60819851.0325.024.0492673.55Machine_Failure-2.449294e-161.000000e+00
3Makino-L2-Unit1-2015Shopfloor-L2139.3400004.5743826.56039424.444.240.60.61830.79118461.0360.025.8600293.55Machine_Failure5.000000e-01-8.660254e-01
4Makino-L1-Unit1-2013Shopfloor-L160.5100006.8931826.1412384.147.331.40.98325.51626526.0354.025.5158743.55Machine_Failure1.000000e+006.123234e-17
5Makino-L2-Unit1-2015Shopfloor-L2137.3700005.9183577.2280665.448.032.70.90325.59727613.0319.025.5213303.55Machine_Failure1.000000e+006.123234e-17
6Makino-L1-Unit1-2013Shopfloor-L1135.9300006.5603326.71099919.348.837.41.24032.13826605.0438.025.4546523.58Machine_Failure1.000000e+006.123234e-17
7Makino-L3-Unit1-2015Shopfloor-L3127.7151645.0607096.00222920.845.837.51.12519.82314266.0334.034.9730042.02No_Machine_Failure1.000000e+006.123234e-17
8Makino-L3-Unit1-2015Shopfloor-L3123.6184565.0743806.0395244.551.532.10.69016.97220413.0278.032.5192992.88No_Machine_Failure1.000000e+006.123234e-17
9Makino-L3-Unit1-2015Shopfloor-L3134.0200005.5678576.73309614.047.935.20.74836.60120504.0379.025.6185673.93Machine_Failure1.000000e+006.123234e-17
Machine_IDAssembly_Line_NoHydraulic_Pressure(bar)Coolant_Pressure(bar)Air_System_Pressure(bar)Coolant_TemperatureHydraulic_Oil_TemperatureSpindle_Bearing_TemperatureSpindle_VibrationTool_VibrationSpindle_Speed(RPM)Voltage(volts)Torque(Nm)Cutting(kN)Downtimemonth_sinmonth_cos
2490Makino-L1-Unit1-2013Shopfloor-L155.5400004.8416056.85228326.442.936.21.43624.81926526.0348.015.1197392.53Machine_Failure0.8660250.5
2491Makino-L2-Unit1-2015Shopfloor-L290.5300006.8394135.6987659.452.540.91.27131.57327613.0377.016.9641053.67Machine_Failure0.8660250.5
2492Makino-L1-Unit1-2013Shopfloor-L1137.3700004.8425216.13658521.549.642.30.43237.42319536.0402.024.4370392.84Machine_Failure0.8660250.5
2493Makino-L3-Unit1-2015Shopfloor-L3106.3888815.2117536.13113522.350.738.70.98936.26517726.0297.031.5210752.02No_Machine_Failure0.8660250.5
2494Makino-L2-Unit1-2015Shopfloor-L2131.8943075.2215556.57339222.445.532.51.27623.43020978.0390.022.7572702.94No_Machine_Failure0.8660250.5
2495Makino-L1-Unit1-2013Shopfloor-L1112.7155065.2208856.19661022.348.837.20.91020.28220974.0282.022.7616102.72No_Machine_Failure0.8660250.5
2496Makino-L1-Unit1-2013Shopfloor-L1103.0866535.2118867.07465311.948.331.51.10634.70820951.0319.022.7865972.94No_Machine_Failure0.8660250.5
2497Makino-L2-Unit1-2015Shopfloor-L2118.6431655.2129916.5300494.549.936.20.28816.82820958.0335.022.778987NaNNo_Machine_Failure0.8660250.5
2498Makino-L3-Unit1-2015Shopfloor-L3145.8558595.2077776.40265512.244.532.10.99526.49820935.0376.022.8040122.79No_Machine_Failure0.8660250.5
2499Makino-L2-Unit1-2015Shopfloor-L296.6900005.9366107.10935529.853.236.20.84031.58023576.0385.024.4095513.55Machine_Failure0.8660250.5